




"""simple docstring"""

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 							lowercase_			(	_A     ,      _A     ,      _A     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	List[str]     =      AltDiffusionPipeline
 UpperCAmelCase       :	Union[str, Any]     =      TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase       :	Union[str, Any]     =      TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase       :	List[str]     =      TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase       :	Tuple     =      TEXT_TO_IMAGE_IMAGE_PARAMS

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       torch.manual_seed(0						)
       _A     =  UNetaDConditionModel(
           block_out_channels=(32, 64)       ,				layers_per_block=2       ,				sample_size=32       ,				in_channels=4       ,				out_channels=4       ,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,				up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,				cross_attention_dim=32       ,				)
       _A     =  DDIMScheduler(
           beta_start=0.0_0085       ,				beta_end=0.012       ,				beta_schedule='scaled_linear'       ,				clip_sample=snake_case_       ,				set_alpha_to_one=snake_case_       ,				)
       torch.manual_seed(0						)
       _A     =  AutoencoderKL(
           block_out_channels=[32, 64]       ,				in_channels=3       ,				out_channels=3       ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,				latent_channels=4       ,				)

       # TODO: address the non-deterministic text encoder (fails for save-load tests)
       # torch.manual_seed(0)
       # text_encoder_config = RobertaSeriesConfig(
       #     hidden_size=32,
       #     project_dim=32,
       #     intermediate_size=37,
       #     layer_norm_eps=1e-05,
       #     num_attention_heads=4,
       #     num_hidden_layers=5,
       #     vocab_size=5002,
       # )
       # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

       torch.manual_seed(0						)
       _A     =  CLIPTextConfig(
           bos_token_id=0       ,				eos_token_id=2       ,				hidden_size=32       ,				projection_dim=32       ,				intermediate_size=37       ,				layer_norm_eps=1E-0_5       ,				num_attention_heads=4       ,				num_hidden_layers=5       ,				pad_token_id=1       ,				vocab_size=5_002       ,				)
       _A     =  CLIPTextModel(snake_case_						)

       _A     =  XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'						)
       _A     =  77

       _A     =  {
           """unet""": unet,
           """scheduler""": scheduler,
           """vae""": vae,
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """safety_checker""": None,
           """feature_extractor""": None,
       }
       return components

 def      lowerCAmelCase_				(					self			:			str       ,				_UpperCAmelCase			:			Any       ,				_UpperCAmelCase			:			Optional[Any]=0						):
       if str(snake_case_						).startswith('mps'						):
             _A     =  torch.manual_seed(snake_case_						)
       else:
             _A     =  torch.Generator(device=snake_case_						).manual_seed(snake_case_						)
       _A     =  {
           """prompt""": """A painting of a squirrel eating a burger""",
           """generator""": generator,
           """num_inference_steps""": 2,
           """guidance_scale""": 6.0,
           """output_type""": """numpy""",
       }
       return inputs

 def      lowerCAmelCase_				(					self			:			int						):
       super().test_attention_slicing_forward_pass(expected_max_diff=3E-3						)

 def      lowerCAmelCase_				(					self			:			str						):
       super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  """cpu"""  # ensure determinism for the device-dependent torch.Generator

       _A     =  self.get_dummy_components()
       torch.manual_seed(0						)
       _A     =  RobertaSeriesConfig(
           hidden_size=32       ,				project_dim=32       ,				intermediate_size=37       ,				layer_norm_eps=1E-0_5       ,				num_attention_heads=4       ,				num_hidden_layers=5       ,				vocab_size=5_002       ,				)
       # TODO: remove after fixing the non-deterministic text encoder
       _A     =  RobertaSeriesModelWithTransformation(snake_case_						)
       _A     =  text_encoder

       _A     =  AltDiffusionPipeline(**snake_case_						)
       _A     =  alt_pipe.to(snake_case_						)
       alt_pipe.set_progress_bar_config(disable=snake_case_						)

       _A     =  self.get_dummy_inputs(snake_case_						)
       _A     =  """A photo of an astronaut"""
       _A     =  alt_pipe(**snake_case_						)
       _A     =  output.images
       _A     =  image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       _A     =  np.array(
           [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2



 def      lowerCAmelCase_				(					self			:			str						):
       _A     =  """cpu"""  # ensure determinism for the device-dependent torch.Generator

       _A     =  self.get_dummy_components()
       _A     =  PNDMScheduler(skip_prk_steps=snake_case_						)
       torch.manual_seed(0						)
       _A     =  RobertaSeriesConfig(
           hidden_size=32       ,				project_dim=32       ,				intermediate_size=37       ,				layer_norm_eps=1E-0_5       ,				num_attention_heads=4       ,				num_hidden_layers=5       ,				vocab_size=5_002       ,				)
       # TODO: remove after fixing the non-deterministic text encoder
       _A     =  RobertaSeriesModelWithTransformation(snake_case_						)
       _A     =  text_encoder
       _A     =  AltDiffusionPipeline(**snake_case_						)
       _A     =  alt_pipe.to(snake_case_						)
       alt_pipe.set_progress_bar_config(disable=snake_case_						)

       _A     =  self.get_dummy_inputs(snake_case_						)
       _A     =  alt_pipe(**snake_case_						)
       _A     =  output.images
       _A     =  image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       _A     =  np.array(
           [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2





@slow
@require_torch_gpu
class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			str						):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

 def      lowerCAmelCase_				(					self			:			int						):
       # make sure here that pndm scheduler skips prk
       _A     =  AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'       ,				safety_checker=snake_case_						)
       _A     =  alt_pipe.to(snake_case_						)
       alt_pipe.set_progress_bar_config(disable=snake_case_						)

       _A     =  """A painting of a squirrel eating a burger"""
       _A     =  torch.manual_seed(0						)
       _A     =  alt_pipe([prompt]       ,				generator=snake_case_       ,				guidance_scale=6.0       ,				num_inference_steps=20       ,				output_type='np'						)

       _A     =  output.images

       _A     =  image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       _A     =  np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2



 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A     =  DDIMScheduler.from_pretrained('BAAI/AltDiffusion'       ,				subfolder='scheduler'						)

       _A     =  AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'       ,				scheduler=snake_case_       ,				safety_checker=snake_case_						)
       _A     =  alt_pipe.to(snake_case_						)
       alt_pipe.set_progress_bar_config(disable=snake_case_						)

       _A     =  """A painting of a squirrel eating a burger"""
       _A     =  torch.manual_seed(0						)

       _A     =  alt_pipe([prompt]       ,				generator=snake_case_       ,				num_inference_steps=2       ,				output_type='numpy'						)
       _A     =  output.images

       _A     =  image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       _A     =  np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2



import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 							_UpperCamelCase					(       _A  ,							_A  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    str          =			IFInpaintingSuperResolutionPipeline
			__UpperCamelCase   :    str          =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
			__UpperCamelCase   :    Optional[Any]          =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}	)
			__UpperCamelCase   :    Dict          =			PipelineTesterMixin.required_optional_params - {"""latents"""}

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							return self._get_superresolution_dummy_components()

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							str=0   ):
							if str(snake_case_   ).startswith("""mps"""   ):
											UpperCamelCase_:				Union[str, Any]						=    torch.manual_seed(snake_case_   )
							else:
											UpperCamelCase_:				Tuple						=    torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )

							UpperCamelCase_:				Dict						=    floats_tensor((1, 3, 16, 16)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				int						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				str						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )

							UpperCamelCase_:				Dict						=    {
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": image,
							    """original_image""": original_image,
							    """mask_image""": mask_image,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """output_type""": """numpy""",
							}

							return inputs

			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()    ,						reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,						)
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_save_load_optional_components()

			@unittest.skipIf(torch_device != """cuda"""    ,						reason="""float16 requires CUDA"""   )
			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1e-1   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_save_load_local()




			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2    ,						)






